Power transmission line loss identification method and device for small-region dynamic integration
By segmenting large areas into similar smaller areas and combining the K-means algorithm with a deep learning model, the problem of low accuracy in identifying large-area transmission line losses was solved, achieving higher identification accuracy.
Patent Information
- Application Number
- CN202512024023.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-21
AI Technical Summary
Existing technologies are not accurate enough for identifying large-area transmission line losses and cannot meet the needs of refined identification. In particular, traditional methods lead to insufficient calculation accuracy under complex distribution network structures.
The large region is divided into several similar small regions. By collecting and preprocessing parameters of transmission lines, power supply terminals, power sales terminals, and environmental energy, a hierarchical association mapping table is established. The K-means algorithm is used to divide the small regions, and line loss is calculated through a deep learning model. Accurate identification is achieved by combining the distribution network structure, power sales similarity, and energy similarity.
It improves the accuracy of transmission line loss identification, avoids interference from complex situations in large areas, and achieves higher analysis accuracy.
Smart Images
Figure CN121901771A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power transmission line loss identification, and in particular to a method and apparatus for power transmission line loss identification for dynamic integration in small areas. Background Technology
[0002] In recent years, driven by both "improved power supply reliability" and "high-proportion integration of new energy sources," the structural complexity and operational uncertainty of power distribution networks have increased significantly. The increasing integration of distributed photovoltaic and other new energy sources into distribution networks has transformed the traditional "single substation power supply" model into a multi-source power supply model of "substation + distributed power sources." Furthermore, current methods for identifying transmission line losses over large areas rely excessively on simplified assumptions (equal resistance method, maximum load loss hour method, etc.), failing to meet the demands for refined identification. While some line loss identification models exist, the complexity of transmission lines over large areas, even with extensive data training, results in low overall training accuracy. Summary of the Invention
[0003] This application provides a method and apparatus for identifying power transmission line losses in small-area dynamic integration, which solves the technical problem that the existing technology has low accuracy in calculating power transmission line losses over large areas.
[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for identifying transmission line losses for dynamic integration in small areas is provided, including: S1: Collect parameters of transmission lines, power supply terminals, electricity sales terminals, and environmental energy resources over a large area; wherein, the transmission line parameters include the distribution network topology, distribution parameters, and tie switch parameters; the power supply parameters include the power supply and active power at the head end of the distribution line; the electricity sales terminal parameters include meter readings and load power; and the environmental energy resources parameters include photovoltaic access data and photovoltaic access ratio. S2: After preprocessing the transmission line parameters, power supply parameters, electricity sales parameters, and environmental energy parameters, align them and establish a hierarchical association mapping table; S3: Divide the large region into several smaller regions based on the hierarchical association mapping table; S4: Analyze the line loss of several small areas to obtain several corresponding sub-line losses; S5: Sum the losses of several sub-lines to obtain the transmission line losses over a large area.
[0005] Based on the above technical solution, in the transmission line loss identification method for dynamic integration of small areas provided in this application, a large area is divided into several small areas according to similar power supply and sales, similar environmental data, or other similar situations. Then, the small areas are analyzed to avoid large differences in line loss caused by the large differences in the specific power consumption of each area. Then, the line loss of the small areas is integrated into the line loss of the large area to reduce the analysis error of the line loss of the large area.
[0006] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the hierarchical association mapping table includes: Data cleaning is performed on transmission line parameters, power supply parameters, electricity sales parameters, and photovoltaic grid connection ratio to obtain cleaned data; After aligning the cleaned data by time, the data is concatenated to obtain a hierarchical association mapping table.
[0007] Furthermore, the method for data concatenation is as follows: each distribution network element is assigned a unique identifier, and a data association mapping table is established according to the hierarchical relationship of "transformers, lines, substations, etc." The incorrect association relationship is corrected through business logic verification to avoid "data source error" in subsequent line loss calculation.
[0008] Data cleaning methods include removing duplicate data, handling missing values, and handling outliers.
[0009] In conjunction with the first aspect above, in one possible implementation, the division of the large region according to the hierarchical association mapping table includes: Establish similarity indicators based on association mapping tables; After standardizing the similarity index, the sub-units are classified using the K-means algorithm to obtain the initial region; Several initial regions are validated, and the validated initial regions are marked as small regions. The boundaries of the unvalidated initial regions are adjusted until they pass the validation, resulting in several small regions.
[0010] In conjunction with the first aspect above, in one possible implementation, the similarity indicators include distribution network structure similarity, arched electricity sales similarity, and energy similarity; The calculation method for the similarity of the power distribution network structure is as follows: ;in, , , The structural weight coefficients, the To ensure consistency in the space frame pattern; , These represent the total number of interconnecting switches within the first and second sub-units, respectively. , These are the total lengths of the wiring within the first and second sub-units, respectively. The average power supply radius deviation rate between the two sub-units. This represents the maximum average power supply radius of all sub-units within the large area. The average power supply radius deviation rate is represented by the sub-unit, which is the smallest unit for dividing a large area into smaller areas. The method for calculating the similarity of the arch-type electricity sales system is as follows: ;in, , The weighting coefficient for electricity sales. The load density deviation rate of the two sub-units. This represents the highest load density within a large area. The power supply and sales differential rate between the two sub-units. This represents the largest differential rate for electricity sales within a large region. The energy similarity is calculated as follows: ;in, The deviation rate of photovoltaic grid connection ratio. This represents the maximum access ratio within a large region.
[0011] In conjunction with the first aspect above, in one possible implementation, the standardization of the similarity index includes: The similarity index is mapped to the [0,1] interval using min-max standardization; where, The value of the similarity index after min-max standardization. For a certain similarity index of sub-units, The minimum value of a certain similarity index. The maximum value of a certain similarity index; The similarity index is standardized using Z-Score standardization to obtain the Z-Score standardized similarity index value. ; Through calculation formula Standardized data is obtained through calculation.
[0012] In conjunction with the first aspect above, in one possible implementation, the classification of sub-units using the K-means algorithm yields several initial regions, including: A1: Determine the number of small areas K based on the scale of the distribution network lines in the large area, and select the initial center of several small areas; A2: Calculate the Euclidean distance between each sub-unit and several initial centers; A3: Based on the Euclidean distance, several sub-units that are not at the initial center are respectively moved to the initial center with the nearest Euclidean distance to obtain several clusters; A4: Calculate the mean of all points in each cluster and update the initial centers; A5: Repeat A3-A4 until the initial center no longer changes or the maximum number of iterations is reached.
[0013] In conjunction with the first aspect above, in one possible implementation, determining the number of small regions K based on standardized data within a large region includes: The sum of standardized data for several sub-units is calculated to obtain the measurement data for several sub-units; the measurement data are arranged in ascending order and linearly fitted to obtain the measurement curve. Obtain the first derivative of the measurement curve, compare the absolute values of the first derivatives of several measurement data with the change threshold; mark the measurement data that are greater than the change threshold as the boundary point, and mark the measurement data that are less than the change threshold as the concentration point; The number of small regions K is determined based on the number of boundary points.
[0014] In conjunction with the first aspect above, in one possible implementation, selecting the initial center of several small regions includes: The measurement data of the small area is determined based on the boundary point, and the minimum distance between the measurement data of all sub-units in the small area and the measurement data of other sub-units is found. The sub-unit corresponding to the minimum distance is marked as the initial center.
[0015] In conjunction with the first aspect above, in one possible implementation, the analysis of line loss in several small areas includes: Line parameters, load data, and environmental energy parameters within a small area are input into a line loss calculation model to obtain the line loss within that area. The line loss calculation model is obtained by training a deep learning model based on sample data. The sample data includes training input data and training output data. The training input data includes sample transmission line parameters, sample power supply parameters, sample electricity sales parameters, and sample environmental energy parameters. The training output data is the line loss corresponding to the training input data.
[0016] Secondly, an electronic device is provided, comprising: a communication unit and a processing unit; the communication unit is used to collect transmission line parameters, power supply end parameters, power sales end parameters, and environmental energy parameters of a large area; the processing unit is used to preprocess the transmission line parameters, power supply end parameters, power sales end parameters, and environmental energy parameters, align them, and establish a hierarchical association mapping table; divide the large area into several smaller areas according to the hierarchical association mapping table; analyze the line loss of the several smaller areas to obtain several corresponding sub-line losses; and sum the several sub-line losses to obtain the transmission line loss of the large area.
[0017] Thirdly, this application provides a processing apparatus, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is configured to execute the instructions to implement the methods described in the first aspect and any possible implementation thereof. The processing apparatus may be an electronic device or a chip within an electronic device.
[0018] Fourthly, this application provides a transmission line loss identification system for dynamic integration in small areas, comprising: a data acquisition module, a partitioning module, and a data analysis module; wherein, the data acquisition module is used to collect transmission line parameters, power supply end parameters, power sales end parameters, and environmental energy parameters of a large area; the partitioning module is used to preprocess the transmission line parameters, power supply end parameters, power sales end parameters, and environmental energy parameters, align them, and establish a hierarchical association mapping table; the large area is divided into several small areas according to the hierarchical association mapping table; the data analysis module is used to analyze the line loss of the several small areas to obtain several corresponding sub-line losses; the several sub-line losses are summed to obtain the transmission line loss of the large area.
[0019] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods described in the first aspect and any possible implementation thereof.
[0020] Sixthly, this application provides a computer program product containing instructions that, when run on an electronic device, cause the electronic device to perform the methods described in the first aspect and any possible implementation thereof.
[0021] This application provides a method and apparatus for identifying transmission line losses in small-area dynamic integration. By dividing a large area with complex and numerous transmission line losses into several smaller areas with similar conditions, and then using a trained line loss identification model to identify the line losses in each smaller area, the method avoids interference between complex situations within the large area, resulting in higher accuracy in line loss identification. Moreover, compared to existing methods that divide large areas into smaller areas using equal division or adjacency principles, this application uses sub-units as the smallest dividing unit and divides them based on the similarity of distribution network structure, power sales, and energy resources between sub-units. This avoids non-adjacent but similar sub-units not being divided into the same area, further ensuring the accuracy of line loss identification.
[0022] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description
[0023] Figure 1 A system architecture diagram for line loss identification provided in this application embodiment; Figure 2 A flowchart illustrating a transmission line loss identification method for dynamic integration in a small area, provided as an embodiment of this application; Figure 3 A schematic diagram illustrating the process of dividing a small region as provided in an embodiment of this application; Figure 4 This is a schematic diagram of the process for obtaining the initial region provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the hardware structure of a processing device provided in an embodiment of this application; Detailed Implementation
[0024] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.
[0025] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.
[0026] The transmission line loss identification method for small-area dynamic integration provided in this application embodiment can be applied to, for example... Figure 1 In the line loss identification system 100 shown, such as Figure 1 As shown, the communication system includes: a data acquisition terminal 10, a data transmission terminal 20, and a cloud processing device 30.
[0027] Among them, the data acquisition terminal 10 is used to acquire the distribution network topology, distribution parameters, tie switch parameters, power supply at the head end of the distribution line, active power, metered power and load power, photovoltaic access data and photovoltaic access ratio of a large area.
[0028] Data transmission terminal 20 is used to send the distribution network topology, distribution parameters, tie switch parameters, power supply, active power, metered power and load power, photovoltaic access data and photovoltaic access ratio of a large area to cloud processing equipment 30.
[0029] The cloud processing device 30 is used to preprocess and align transmission line parameters, power supply parameters, electricity sales parameters, and environmental energy parameters, and establish a hierarchical association mapping table; the large area is divided into several small areas according to the hierarchical association mapping table; the line loss of several small areas is analyzed to obtain several corresponding sub-line losses; and the transmission line loss of the large area is obtained by summing the sub-line losses.
[0030] To address the technical problem of low accuracy in calculating transmission line losses over large areas in existing technologies, this application provides a method for identifying transmission line losses for dynamic integration in small areas. The method includes: S1: collecting transmission line parameters, power supply end parameters, power sales end parameters, and environmental energy parameters for a large area; wherein, the transmission line parameters include the distribution network topology, distribution parameters, and tie switch parameters; The power supply parameters include the power supply and active power at the beginning of the power distribution line; The parameters of the electricity sales terminal include the metered electricity consumption and the load power; The environmental energy parameters include photovoltaic access data and photovoltaic access ratio; S2: After preprocessing the transmission line parameters, power supply parameters, electricity sales parameters, and environmental energy parameters, align them and establish a hierarchical association mapping table; S3: Divide the large region into several smaller regions based on the hierarchical association mapping table; S4: Analyze the line loss of several small areas to obtain several corresponding sub-line losses; S5: Summing up the losses of several sub-lines yields the transmission line loss for the large area. Based on this, the large area is divided into several similar smaller areas, and the line loss of each smaller area is identified separately, which improves the accuracy of line loss identification for each smaller area.
[0031] like Figure 2 As shown in the embodiment of this application, a method for identifying transmission line losses for dynamic integration in small areas includes: S201. Collect parameters of transmission lines, power supply terminals, electricity sales terminals, and photovoltaic grid connection ratios for large areas. The transmission line parameters include the distribution network topology, distribution parameters, and tie switch parameters; the power supply parameters include the power supply and active power at the head end of the distribution line; the electricity sales terminal parameters include meter readings and load power; and the environmental energy parameters include photovoltaic access data and photovoltaic access ratio.
[0032] In this embodiment, the power distribution network topology includes the route of the power distribution lines (such as the line extending from substation A to community B), the node connection relationship (such as line intersection nodes and branch nodes), and the power supply range of each power distribution line (such as the power supply radius of line J is 2.5km, covering community K and factory L, and driving transformers No. 1 and No. 2 in community H). The parameters of the tie switch include the location of the tie switch (such as the 10kV tie switch at intersection C), the distribution of the switch station (such as the 10kV switch station in area D), and the cross-regional tie structure (such as the hand-in-hand line connecting area E and area F in a large area). The power distribution parameters include power line parameters (e.g., the conductor type of line G in the large area is JKLYJ-10-120, the length is 5km, and the unit impedance r=0.27Ω / km), power distribution transformer parameters (e.g., the capacity of transformer No. 1 in H area in the large area is 500kVA, the iron loss is 800W, and the copper loss is 4500W), and the parameters of all tie switches (e.g., the rated current of tie switch I in the large area is 630A, and the operating status is normally closed). Environmental energy parameters include photovoltaic (PV) grid connection data (e.g., the grid connection location of the rooftop PV system of Factory S in the large area is the low-voltage side of the distribution transformer of Factory S, with an impact capacity of 1MW; 24-hour output curve: 600kW at 9:00, 950kW at 12:00, and 700kW at 16:00), and PV grid connection ratio (e.g., the total installed PV capacity in Area T is 5MW, the maximum load in the area is 10MW, and the grid connection ratio is 50%). For example, the power supply at the beginning of a distribution line, such as the 24-hour hourly power supply at the beginning of line N, is 120MWh at 8:00 and 150MWh at 9:00; the active power is 500kW at 8:00 and 620kW at 9:00. This data can be sourced from the dispatching systems of various substations within a large region. Meter readings, such as the monthly electricity consumption of transformer No. 3 in P community within the large area, are 80MWh, and the load power is 300kW at 18:00 and 180kW at 22:00. The data can be obtained from user meter data of the marketing system within the large area. For example, in the Q area, the total installed photovoltaic capacity is 5MW, the maximum load of the area is 10MW, and the grid connection ratio is 50%.
[0033] For example, to eliminate duplicate data: the dispatch system records "the power at the beginning of the 10kV A line is 500kW", and the marketing system calculates "the total load of the 10kV A line is 480kW" through transformer aggregation. Then, the data source with higher accuracy is used as the benchmark (the real-time power accuracy of the dispatch system is higher than that of the marketing aggregation data), and deviation verification is performed. If the deviation is ≤5% (the allowable error for distribution network load statistics), the dispatch data is retained; if the deviation is >5%, the cause is investigated. If the dispatch power of the 10kV A line is 500kW and the marketing aggregation is 480kW (deviation 4%), the dispatch data is retained; if the marketing aggregation is 300kW (deviation 40%), the transformer attribution in the marketing system is checked for errors.
[0034] S202: After preprocessing the transmission line parameters, power supply parameters, electricity sales parameters, and environmental energy parameters, align them and establish a hierarchical association mapping table; Furthermore, the methods for obtaining the hierarchical association mapping table include: Data cleaning is performed on transmission line parameters, power supply parameters, electricity sales parameters, and photovoltaic grid connection ratio to obtain cleaned data; After aligning the cleaned data by time, the data is concatenated to obtain a hierarchical association mapping table.
[0035] Data cleaning methods include removing duplicate data, handling missing values, and handling outliers.
[0036] Handling missing values: The conductor model of "10kV Line B" in the equipment system is recorded as JKLYJ-10-150, but the unit impedance r is not entered; a certain transformer only records a capacity of 500kVA, without iron loss parameters; then look up the standard parameter table according to the equipment model (e.g., the standard unit impedance of JKLYJ-10-150 is 0.21Ω / km). If there is no standard table, supplement it with the average parameter value of equipment of the same model and manufacturer.
[0037] Outlier Handling: Power anomaly: The power at the beginning of the line is -100kW (the actual power supply cannot be negative unless the photovoltaic power is reversed, but this needs to be judged in conjunction with the time period); Voltage anomaly: The voltage at the node of the 10kV line is 15kV (exceeding the allowable voltage range of 9.3-10.7kV for 10kV lines); Photovoltaic output anomaly: The photovoltaic output at 2:00 AM is 200kW (the output should be 0 when there is no sunlight). Then, the following checks are performed according to the business rules: Power: Power supply end power ≥ 0, photovoltaic output ≥ 0 during the day and = 0 at night; Voltage: 10kV line voltage ∈ [9.3, 10.7]kV, 0.4kV line ∈ [0.38, 0.42]kV; The 3σ principle can also be used to assist in the judgment. The mean μ and standard deviation σ of a certain indicator are calculated. If they exceed [μ-3σ, μ+3σ], they are judged as anomalies.
[0038] Furthermore, the method for data concatenation is as follows: each distribution network element (line, transformer, switch, etc.) is assigned a unique identifier, and a data association mapping table is established according to the hierarchical relationship of "transformer, line, substation, etc." The incorrect association relationship is corrected through business logic verification (such as mistakenly associating a transformer that does not belong to a certain line), so as to avoid "data source error" when calculating line loss in the future.
[0039] Extract line-transformer associations (such as line ID, line name, transformer ID, transformer name, transformer installation address, associated line, and power supply at the beginning of the line) from the equipment management system and dispatch system; extract transformer-load associations (such as transformer ID, transformer name, user ID, user meter number, user address, user electricity category (residential / industrial), and user monthly electricity meter reading) from the marketing system; verify the line-transformer associations and transformer-load associations, and finally output the mapping table as shown in Table 1; Table 1
[0040] It should be noted that the line-transformer verification logic can be: power supply at the beginning of the line (dispatch system) ≈ total electricity metered by all transformers under this line (marketing system) + line technical loss (estimated value), with an allowable deviation of ≤5%; The transformer-load correlation verification logic can be: the input power of the transformer high-voltage side (dispatch system) ≈ the total power consumption of all users under the transformer (real-time monitoring by the marketing system) + transformer loss (estimated value), with an allowable deviation of ≤8% (transformer loss is usually 3%-5%).
[0041] S203. Divide the large region into several smaller regions according to the hierarchical association mapping table.
[0042] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3As shown, the above S203 can be implemented through the following S301, S302 and S303, which are explained in detail below: S301. Establish similarity indicators based on the association mapping table;
[0043] The similarity indicators include distribution network structure similarity, arc-style electricity sales similarity, and energy similarity. Specifically, the calculation method for the similarity of power distribution network structures is as follows: ;in, , , The structural weight coefficients, the To ensure consistency in the space frame pattern; , These represent the total number of interconnecting switches within the first and second sub-units, respectively. , These are the total lengths of the wiring within the first and second sub-units, respectively. The average power supply radius deviation rate between the two sub-units. This represents the maximum average power supply radius of all sub-units within the large area. The sub-unit represents the average power supply radius deviation rate. It is the smallest unit for dividing a large area into smaller areas, and this smallest unit can be a residential community or a park. For the interconnection switch density within the first sub-unit, This refers to the density of interconnection switches within the second sub-unit.
[0044] The calculation method for the similarity of electricity sales through arches is as follows: ;in, , The weighting coefficient for electricity sales. The load density deviation rate of the two sub-units. This represents the highest load density within a large area. The power supply and sales differential rate between the two sub-units. This represents the largest differential rate for electricity sales within a large region. The energy similarity is calculated as follows: ;in, The deviation rate of photovoltaic grid connection ratio. This represents the maximum access ratio within a large region.
[0045] It should be noted that the consistency of the network structure pattern is determined based on the network structure pattern of the sub-unit. For example, if two sub-units have the same network structure pattern (e.g., both are double-ring networks), a value of 1 is assigned; if they have similar patterns (e.g., double-ring network and single-ring network), a value of 0.6 is assigned; if they have large differences (e.g., double-ring network and radial network), a value of 0.2 is assigned. The above-mentioned structural weight coefficient and arch-type power sales weight coefficient can be weighted by the analytic hierarchy process or by the scoring of distribution network experts.
[0046] S302. After standardizing the similarity index, the sub-units are classified using the K-means algorithm to obtain the initial region; In some implementations, similarity metrics are standardized, including: The similarity index is mapped to the [0,1] interval using min-max standardization; where, The value of the similarity index after min-max standardization. For a certain similarity index of sub-units, The minimum value of a certain similarity index. The maximum value of a certain similarity index; The similarity index is standardized using Z-Score standardization to obtain the Z-Score standardized similarity index value. ; Through calculation formula Standardized data is obtained through calculation.
[0047] It should be noted that standardized data includes standardized data on the similarity of distribution network structure, standardized data on the similarity of electricity sales, and standardized data on energy similarity.
[0048] Furthermore, the sub-units are classified using the K-means algorithm to obtain several initial regions, including: A1: Determine the number of small areas K based on the scale of the distribution network lines in the large area, and select the initial center of several small areas; A2: Calculate the Euclidean distance between each sub-unit and several initial centers; A3: Based on the Euclidean distance, several sub-units that are not at the initial center are respectively moved to the initial center with the nearest Euclidean distance to obtain several clusters; A4: Calculate the mean of all points in each cluster and update the initial centers; A5: Repeat A3-A4 until the initial center no longer changes or the maximum number of iterations is reached.
[0049] Furthermore, the number of small regions K is determined based on standardized data within the large region, including: The sum of standardized data for several sub-units is calculated to obtain the measurement data for several sub-units; the measurement data are arranged in ascending order and linearly fitted to obtain the measurement curve. Obtain the first derivative of the measurement curve, compare the absolute values of the first derivatives of several measurement data with the change threshold; mark the measurement data that are greater than the change threshold as the boundary point, and mark the measurement data that are less than the change threshold as the concentration point; The number of small regions K is determined based on the number of boundary points.
[0050] It should be noted that the sub-unit corresponding to the measurement data between two dividing points is a small region, and the interval between the dividing points at both ends and 0 and to infinity is also a small region; thus, the number of small regions can be determined according to the number of dividing points.
[0051] The threshold for change can be determined either by setting it manually or by the mean of the absolute values of the first derivatives of several measurement data.
[0052] Furthermore, select initial centers for several small regions, including: The measurement data of the small area is determined based on the boundary point, and the minimum distance between the measurement data of all sub-units in the small area and the measurement data of other sub-units is found. The sub-unit corresponding to the minimum distance is marked as the initial center.
[0053] S303. Verify several initial regions, mark the initial regions that pass the verification as small regions, and adjust the boundaries of the initial regions that fail the verification until they pass the verification, thus obtaining several small regions.
[0054] For example, the verification method is to check whether there is a sudden change in the proportion of new energy access in a small area (such as the coexistence of photovoltaic proportions of 0.1 and 0.8 in a small area).
[0055] S204. Analyze the line loss of several small areas to obtain several corresponding sub-line losses.
[0056] In some implementations, line parameters, load data, and environmental energy parameters within a small area are input into a line loss calculation model to obtain the line loss within that area. The line loss calculation model is obtained by training a deep learning model based on sample data. The sample data includes training input data and training output data. The training input data includes sample transmission line parameters, sample power supply parameters, sample electricity sales parameters, and sample environmental energy parameters. The training output data is the line loss corresponding to the training input data.
[0057] S205. Summing up the losses of several sub-lines yields the transmission line losses for the large area.
[0058] The foregoing primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as an electronic device, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the aforementioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0059] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.
[0060] When using integrated units, Figure 5 A possible structural schematic diagram of the electronic device (referred to as electronic device 50) involved in the above embodiments is shown. The electronic device 50 includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 5 The structural diagram shown can be used to illustrate the structure of the electronic device involved in the above embodiments.
[0061] when Figure 5 The schematic diagram shown is used to illustrate the structure of the electronic device involved in the above embodiments. The processing unit 501 is used to control and manage the operation of the electronic device, the communication unit 502 is used for the electronic device to communicate with other devices, and the storage unit 503 is used to store the program code and data of the electronic device.
[0062] For example, communication unit 502 is used to collect parameters of power transmission lines, power supply terminals, power sales terminals, and environmental energy in a large area; Processing unit 502 is used to preprocess transmission line parameters, power supply end parameters, power sales end parameters and environmental energy parameters, align them and establish a hierarchical association mapping table; divide the large area into several small areas according to the hierarchical association mapping table; analyze the line loss of several small areas to obtain several corresponding sub-line losses; sum the several sub-line losses to obtain the transmission line loss of the large area.
[0063] The processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 503 can be a memory. When the electronic device 50 is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 503 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.).
[0064] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the electronic device 50 can be considered as the communication unit 502 of the electronic device 50, and the processor with processing functions can be considered as the processing unit 501 of the electronic device 50. Optionally, the device in the communication unit 502 that implements the receiving function can be considered as a communication unit. The communication unit is used to execute the receiving steps in the embodiments of this application, and the communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 502 that implements the transmitting function can be considered as a transmitting unit. The transmitting unit is used to execute the transmitting steps in the embodiments of this application, and the transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.
[0065] Figure 5 If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.
[0066] Figure 5 The units in the process can also be called modules; for example, a processing unit can be called a processing module.
[0067] This application also provides a hardware structure diagram of a processing device (referred to as processing device 60), see [link to diagram]. Figure 6The processing device 60 includes a processor 601, and optionally, a memory 602 connected to the processor 601.
[0068] In the first possible implementation, see Figure 6 The device 60 also includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or communication networks. Optionally, the transceiver 603 may include a transmitter and a receiver. The device in the transceiver 603 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 603 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.
[0069] Based on the first possible implementation method Figure 6 The schematic diagram shown can be used to illustrate the structure of the processing device involved in the above embodiments.
[0070] in, Figure 6 Alternatively, the system chip in the processing device can be illustrated. In this case, the actions performed by the aforementioned processing device can be implemented by the system chip, and the specific actions performed can be found above, and will not be repeated here.
[0071] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.
[0072] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.
[0073] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.
[0074] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.
[0075] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.
[0076] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor, which is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.
[0077] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).
[0078] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.
[0079] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.
Claims
1. A method for identifying power transmission line losses for dynamic integration in small areas, characterized in that, include: The system collects parameters for transmission lines, power supply terminals, electricity sales terminals, and environmental energy across a large region. The transmission line parameters include the distribution network topology, distribution parameters, and tie switch parameters. The power supply parameters include the power supply and active power at the head of the distribution line. The electricity sales terminal parameters include meter readings and load power. The environmental energy parameters include photovoltaic (PV) grid connection data and the PV grid connection ratio. After preprocessing the parameters of transmission lines, power supply terminals, electricity sales terminals, and environmental energy, the parameters are aligned and a hierarchical association mapping table is established. The large region is divided into several smaller regions based on the hierarchical association mapping table; By analyzing the line loss in several small areas, several corresponding sub-line losses are obtained; The transmission line loss of a large area is obtained by summing the losses of several sub-lines.
2. The method for identifying transmission line losses for dynamic integration in small areas according to claim 1, characterized in that, The process of dividing a large region based on a hierarchical association mapping table includes: Establish similarity indicators based on association mapping tables; After standardizing the similarity index, the sub-units are classified using the K-means algorithm to obtain the initial region; Several initial regions are verified, and the initial regions that pass the verification are marked as small regions. The sub-units of the initial regions that fail the verification are adjusted until they pass the verification, thus obtaining several small regions.
3. The method for identifying transmission line losses for dynamic integration in small areas according to claim 2, characterized in that, The similarity indicators include distribution network structure similarity, arc-style electricity sales similarity, and energy similarity; The calculation method for the similarity of the power distribution network structure is as follows: ;in, , , The structural weight coefficients, the To ensure consistency in the space frame pattern; , These represent the total number of interconnecting switches within the first and second sub-units, respectively. , These are the total lengths of the wiring within the first and second sub-units, respectively. The average power supply radius deviation rate between the two sub-units. This represents the maximum average power supply radius of all sub-units within the large area. The average power supply radius deviation rate is defined as the sub-unit, which is the smallest unit for dividing a large area into smaller areas. The method for calculating the similarity of the arch-type electricity sales system is as follows: ;in, , The weighting coefficient for electricity sales. The load density deviation rate of the two sub-units. This represents the highest load density within a large area. The power supply and sales differential rate between the two sub-units. This represents the largest differential rate for electricity sales within a large region. The energy similarity is calculated as follows: ;in, The deviation rate of photovoltaic grid connection ratio. This represents the maximum access ratio within a large region.
4. The method for identifying transmission line losses for dynamic integration in small areas according to claim 2, characterized in that, The standardization of similarity metrics includes: The similarity index is mapped to the [0,1] interval using min-max standardization; where, The value of the similarity index after min-max standardization. For a certain similarity index of sub-units, The minimum value of a certain similarity index. The maximum value of a certain similarity index; The similarity index is standardized using Z-Score standardization to obtain the Z-Score standardized similarity index value. ; Through calculation formula Standardized data is obtained through calculation.
5. The method for identifying transmission line losses for dynamic integration in a small area according to claim 2, characterized in that, The classification of sub-units using the K-means algorithm includes: A1: Determine the number of small areas K based on the scale of the distribution network lines in the large area, and select the initial center of several small areas; A2: Calculate the Euclidean distance between each sub-unit and several initial centers; A3: Based on the Euclidean distance, several sub-units that are not at the initial center are respectively moved to the initial center with the nearest Euclidean distance to obtain several clusters; A4: Calculate the mean of all points in each cluster and update the initial centers; A5: Repeat A3-A4 until the initial center no longer changes or the maximum number of iterations is reached, to obtain several initial regions.
6. The method for identifying transmission line losses for dynamic integration in a small area according to claim 5, characterized in that, The process of determining the number of small regions K based on standardized data within a large region includes: The sum of standardized data for several sub-units is calculated to obtain the measurement data for several sub-units; the measurement data are arranged in ascending order and linearly fitted to obtain the measurement curve. Obtain the first derivative of the measurement curve, compare the absolute value of the first derivative of several measurement data with the change threshold; mark the measurement data that are greater than the change threshold as the boundary point; The number of small regions K is determined based on the number of boundary points.
7. The method for identifying transmission line losses for dynamic integration in a small area according to claim 6, characterized in that, The selection of the initial center of several small regions includes: The measurement data of the small area is determined based on the boundary point, and the minimum distance between the measurement data of all sub-units in the small area and the measurement data of other sub-units is found. The sub-unit corresponding to the minimum distance is marked as the initial center.
8. The method for identifying transmission line losses for dynamic integration in small areas according to claim 1, characterized in that, The analysis of line loss in several small areas includes: Line parameters, load data, and environmental energy parameters within a small area are input into a line loss calculation model to obtain the line loss within that area. The line loss calculation model is obtained by training a deep learning model based on sample data. The sample data includes training input data and training output data. The training input data includes sample transmission line parameters, sample power supply parameters, sample electricity sales parameters, and sample environmental energy parameters. The training output data is the line loss corresponding to the training input data.
9. The method for identifying transmission line losses for dynamic integration in small areas according to claim 1, characterized in that, The methods for obtaining the hierarchical association mapping table include: Data cleaning is performed on transmission line parameters, power supply parameters, electricity sales parameters, and photovoltaic grid connection ratio to obtain cleaned data; After aligning the cleaned data by time, the data is concatenated to obtain a hierarchical association mapping table.
10. An electronic device, characterized in that, include: Communication unit and processing unit; The communication unit is used to collect parameters of power transmission lines, power supply terminals, power sales terminals, and environmental energy resources over a large area. The processing unit is used to preprocess the transmission line parameters, power supply end parameters, electricity sales end parameters and environmental energy parameters, align them and establish a hierarchical association mapping table. The large region is divided into several smaller regions based on the hierarchical association mapping table; By analyzing the line loss in several small areas, several corresponding sub-line losses are obtained; The transmission line loss of a large area is obtained by summing the losses of several sub-lines.